Remote-sensing images

Rapid response and efficient damage assessment are life-or-death matters in the wake of natural disasters such as hurricanes and earthquakes. These events wreak havoc on infrastructure and properties and, most critically, endanger human lives. The timely and effective allocation of resources during such crises is imperative, necessitating meticulous planning based on the extent of damage incurred. This research presents an approach to automating the damage assessment using pre/post-disaster aerial images and computer vision. Recent advancements in disaster response strategies have encouraged researchers to harness the power of satellite and aerial imagery to assess the aftermath. Usually, due to the different characteristics between training datasets and available datasets in times of disasters, retraining the model to improve detection accuracy has been the norm, even though it is time and resource intensive. Our method surpasses conventional solutions and requires no retraining or fine-tuning on disaster-specific data. An existing model was retrained and improved on a diverse building damage dataset and demonstrably generalizes to new disaster scenarios. Having achieved higher performances compared to state of the art models, we determines our models real world applicability by using Hurricane Ian as our potent study grounds.

Digital orthophoto quadrangle quarter (DOQQ) is a new type of imagery. The DOQQ is a product of the United States Geological Survey, which has many uses. The spectral and spatial qualities of a DOQQ are equal to and surpass the qualities of traditional satellite imagery. These qualities can be demonstrated by answering the following questions: (1) Is the quality of the spectral information on a color infrared DOQQ comparable to a SPOT and TM Landsat satellite imagery for the purpose of digital image classification? (2) What are the optimal number of classes for achieving an accuracy supervised classification of a one-meter resolution DOQQ? (3) What is the effect of changing the spatial resolution on image classification of a DOQQ?

The imminent availability of high resolution satellite imagery is causing a paradigm shift in remote sensing. Detente brought about new policy directives in the U.S. and abroad, which opened up for civilian use former Earth observation spy technology down to one meter resolution, previously considered classified and strictly used by the intelligence communities for national security. This study describes a number of new ventures in the private sector which have been formed to launch commercial high resolution systems. The satellites' technical capabilities are analyzed, and application development options for the new imagery are discussed in detail. This new remote sensing data source is also seen within the framework of the larger GeoTechnology Industry to which it belongs, and the author proposes appropriate business strategies for successful commercialization.

Fine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of within-class spectral variability and between-class spectral similarity of many types of land cover leads to low classification accuracy when pixel-based, purely spectral classification techniques are used. Object-based classification methods, which involve segmenting an image into relatively homogeneous regions (i.e. image segments) prior to classification, have been shown to increase classification accuracy by incorporating the spectral (e.g. mean, standard deviation) and non-spectral (e.g. te xture, size, shape) information of image segments for classification. One difficulty with the object-based method, however, is that a segmentation parameter (or set of parameters), which determines the average size of segments (i.e. the segmentation scale), is difficult to choose. Some studies use one segmentation scale to segment and classify all types of land cover, while others use multiple scales due to the fact that different types of land cover typically vary in size. In this dissertation, two multi-scale object-based classification methods were developed and tested for classifying high resolution images of Deerfield Beach, FL and Houston, TX. These multi-scale methods achieved higher overall classification accuracies and Kappa coefficients than single-scale object-based classification methods.